*Corr. Author’s Address: Le Quy Don Technical University, 236 Hoang Quoc Viet, Ha Noi, VietNam, trungthanhnguyen@lqdtu.edu.vn 155
Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165 Received for review: 2021-11-10
© 2022 The Authors. CC BY 4.0 Int. Licensee: SV-JME Received revised form: 2022-01-10
DOI:10.5545/sv-jme.2021.7473 Original Scientific Paper Accepted for publication: 2022-02-01
Investigation and Optimization of MQL System Parameters  
in the Roller-Burnishing Process of Hardened Steel
Le, V .-A. – Nguyen, T.-T.
An-Le Van
1
 – Trung-Thanh Nguyen
2,*
1
Nguyen Tat Thanh University, Faculty of Engineering and Technology, Vietnam 
2
Le Quy Don Technical University, Faculty of Mechanical Engineering, Vietnam
In the current study, the internal burnishing process under the minimum quantity lubrication (MQL) condition has been optimized to 
decrease the cylindricity (CYL) and circularity (CIC) of the burnished hole, while the surface roughness (SR) is predefined as a constraint. 
The optimizing inputs are the diameter of the spray nozzle (D), the spray elevation angle (A), the lubricant quantity (Q), and the pressure 
value of the compressed air (P). The artificial neural network (ANN) models of burnishing performances are proposed to optimise inputs. 
The grey relational analysis (GRA) is utilized to compute the weight value of each response. Optimal values of MQL system parameters and 
technological objectives are selected with the aid of an evolution algorithm (vibration and communication particle swarm optimization (VCPSO) 
algorithm). The results indicated that the optimal outcomes of the D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 MPa, respectively. 
Furthermore, the CYL, CIC, and SR were decreased by 53.14 %, 57.83 %, and 72.97 %, respectively, at the optimal solution. Finally, the 
obtained results are expected to be a significant solution to support the machine operator in selecting the optimal MQL system parameters to 
improve the hole quality in the MQL-assisted burnishing process.
Keywords: internal burnishing; cylindricity; circularity; roughness; ANN; VCPSO  
Highlights
• 	 Optimization of MQL system parameters, including the diameter of the spray nozzle, spray elevation angle, lubricant quantity, 
and pressure value of the compressed air.
• 	 Consideration of the cylindricity, circularity, and surface roughness. 
• 	 Development of ANN models for burnishing responses.
• 	 Selection of optimal MQL parameters using VCPSO.
0  INTRODUCTION
The coolant was widely applied to decrease the 
temperature and friction for different machining 
operations. However, the massive application of 
the lubricant increases its cost far higher than the 
cost of cutting tools, endangers human health, and 
creates environmental pollution [1]. Manufacturing 
costs, health, and environmental issues motivate 
manufacturing enterprises to reduce and eliminate 
lubricants. Dry machining is the manufacturing 
operation without the use of any lubricant, which 
is becoming popular due to environmental safety 
and worker health [2]. Unfortunately, this method is 
restricted to the machining of low-strength material 
with moderate cutting conditions [3] and [4]. An 
alternative solution is minimum quantity lubrication 
(MQL), in which a very low amount of fluid (5 ml/h 
to 200 ml/h) is used in conjunction with compressed 
air. For different machining of hardened steels and 
titanium alloys, MQL is a feasible, efficient, and 
eco-friendly alternative, compared to dry and flood-
cooling methods [5]. 
The MQL method was widely utilized to boost 
the technical performances of different machining 
processes. The optimal values of the lubricant 
quantity (Q), cutting speed (V), and feed rate (f) 
were selected to decrease the cutting force (CF) of 
the milling AISI 4140 steel [6]. The specific cutting 
energy (SCE) model was developed in terms of the V, 
f, Q, and depth of cut (d) for the MQL end milling 
process [7]. Moreover, the MQL resulted in 33 % 
less power consumption (PW) in comparison with 
the wet approach [8]. Rajan et al. [9] emphasized 
that the MQL exhibited 157.3 % and 40 % less tool 
wear (TW) for the turning operation in dry and flood 
conditions, respectively. Sivaiah et al. [10] revealed 
that the SR and TW were decreased by 54 % and 7 %, 
respectively, while the material removal rate (MRR) 
was improved by 27 % for the MQL turning AISI 304. 
Moreover, the MQL mode could help increase the feed 
rate by 40 % in the turning EN31 steel [11]. Zhang 
et al. [12] indicated that the specific grinding energy 
was significantly decreased using nanoparticle-based 
MQL. The combination of the MQL and cooled air 
could be applied to decrease 10.5 % grinding force 
and 36.3 % roundness errors [13]. A hybrid approach 
comprising the MQL and water could provide higher 
cooling-lubrication efficiency [14]. Faverjon et al. 
[15] stated that the MQL was effectively applied 

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
156 Le, V.-A. – Nguyen, T.-T.
to decrease the thermal distortion and enhance the 
machined accuracy of the drilled aluminium part. 
The SR and burr height (BH) of the drilled hole were 
significantly reduced under the MQL condition [16]. 
MQL could be employed to improve the surface 
morphologies for the drilled composites [17].
The employment of the MQL system in 
burnishing operations has been rarely addressed in 
published investigations. The MQL-based diamond-
burnishing process was developed and optimized, in 
which the optimal process parameters, including the 
spindle speed (S), burnishing force (F
b
), and f, were 
selected to decrease the SR and improve the surface 
hardness (SH) [18]. Similarly, the surface integrity 
of the burnished titanium alloy was significantly 
improved using optimal MQL conditions [19]. As a 
result, missing gaps of the previous publications can 
be expressed as follows.
The influences of MQL operating parameters, 
including the nozzle diameter, spray angle, lubricant 
quantity, and air pressure on geometric errors, have 
not been analysed in the published investigations. 
Moreover, predictive models of the cylindricity and 
circularity regarding the MQL operating parameters 
for the burnishing operation have not been proposed.
Fig. 1.  Burnishing experiment
The selection of optimal MQL operating 
parameters for decreasing geometrical deviations 
(cylindricity and circularity) and surface roughness 
for the burnishing operation has not been addressed. 
The diameter and number of droplets were primarily 
affected by the lubricant quantity, air pressure, and 
nozzle diameter, while the machining zone wettability 
was mainly influenced by the nozzle distance and 
lubricant quantity [20] and [21]. The spraying angle 
was related to the workpiece to be machined [22]. 
Therefore, the optimum setting of MQL system 
parameters may provide better penetration of the 
lubricant at the rollers-workpiece interfaces.
1  MATERIAL METHOD
Hardened specimens labelled 5145 are drilled and 
turned using a computer numerical control (CNC) 
lathe machine (CTX 400E). The drill tool with a 
diameter of 16 mm is used to produce the rough hole, 
in which a feed rate of 0.5 mm/rev, a depth of cut of 
8 mm, and a spindle speed of 400 RPM are used as 
the drilling parameters. Multiple turning paths using 
polycrystalline cubic boron nitride (CBN) inserts are 
adopted to turn the internal holes, in which a feed 
rate of 0.25 mm/rev, a depth of cut of 1 mm, and a 
spindle speed of 800 RPM are turning factors. The 
length, internal diameter, and external diameter of 
each workpiece are 60 mm, 28 mm, and 40 mm, 
respectively. The cylindricity, circularity, and surface 
roughness of the pre-machined hole are 70.62 µm, 
18.48 µm, and 3.84 µm, respectively. 
The burnishing trials are done with the aid of a 
milling machine, in which the workpiece is positioned 
and tightly clamped using a jaw-centring chuck (Fig. 
1). Soybean oil has been selected as the lubricant and 
mixed with the compressed air to form the mixture 
(air-oil mist), which is delivered into the machining 
region through MQL nozzles. The Mitutoyo 
Surftest-301 and ZEISS CONTURA G2 are employed 
to capture the surface roughness, cylindricity, and 
circularity values.  
2  OPTIMIZING FRAMEWORK
The schematic illustration of the internal roller 
burnishing operation under the MQL condition is 
depicted in Fig. 2. The MQL system parameters, 
including the diameter of the spray nozzle, the 
spray elevation angle, the lubricant quantity, and the 
pressure value of the compressed air, are presented in 
Table 1. The ranges of each factor are determined with 
the aid of the MQL system’s characteristics and the 
manufacturer’s recommendations. 
Table 1.  MQL system parameters 
Symbol Parameters 1 2 3
D Nozzle diameter [mm] 0.5 1.0 1.5
A Spray angle [deg] 25 45 65
Q Lubricant quantity [ml/h] 40 90 140
P Air pressure [MPa] 0.2 0.4 0.6

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
157 Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel 
Fig. 2.  The schematic illustration of the internal burnishing 
operation
Fig. 3.  The optimization approach fo r the internal burnishing 
operation
The optimization procedure is presented in Fig. 3, 
which is shown as bellows:
Step 1: The burnishing experiments are executed 
based on the Box-Behnken design to save the 
experimental cost [23] and [24]. 
Step 2: The CYL, CIC, and SR models are 
developed in terms of the MQL system parameters 
using the ANN approach [25]. The output of the 
neuron is a function of all inputs and their weights and 
expressed as [26]:
 Ii nput w
ji ij
  , (1)
 Output fI t
jn jj
 () , (2)
where I
j
, w
ij
, t
i
, and f
n
 present the internal value, 
weight, threshold value, and transfer function, 
respectively. 
The numerical simulations of the ANN model are 
conducted to calculate the percentage error (PE): 
 PD
yy
y
ap
a

 





100, (3)
where y
a
 and y
p
 are actual and predicted data, 
respectively. The best architecture of the ANN model 
with the PE value less than 10 %. 
The R
2
 value and root mean square error (RMSE) 
are named as important indicators to investigate the 
adequacy of the developed models and computed as: 
 R
yy
y
pi ai
ai
i
n
i
n
2 1
2
1
2
1 
 













()
()
, (4)
 RMSE
n
yy
pi ai
i
n
(%) . 



1 2
1
 (5)
Step 3: The weight value of each response is 
determined using the grey relation analysis (GRA). 
The correlation coefficient is calculated as:
 S
jl
ii
Ii Ii
CovI jIl
jl








(( ), () )
() ()
,

 (6)
where Cov(I
i
(j)) and I
i
(l) presents the covariance of 
sequences I
i
(j) and I
i
(l), respectively. 
The eigenvalues and consequent eigenvectors are 
calculated as:
 () , SJ V
km ik
  0 (7)
where λ
k
, V
ik
, and J
m
 presents the eigenvalues, 
eigenvectors, and the identity matrix, respectively.
The major principal coefficient is calculated as:
 PC IiV
mm
i
n
ik
 
1
() , (8)
Step 4: The optimal outcomes of the MQL system 
factors and technical responses are selected using the 
VCPSO algorithm. Practically, the results produced 
by the PSO are easily failed into the local optimizing 
outcomes due to the lack of communication among 
entire particles. To obtain the global solution, 
communication with three operations (i.e., the 
mutation, crossover, and selection) is performed.  
The mutation is conducted to exchange 
information between old and new particles. The 
mutation operator is expressed as:  

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
158 Le, V.-A. – Nguyen, T.-T.
 xx Fx x
i
son
ra rb rc
  () , (9)
where ra, rb, and rc are random integers in the range 
(0, N). The F is the scaling factor. 
In the crossover stage, the trial vector u
i
son
 is 
produced to increase the diversity of the population 
on the basis of the mutation operator. The crossover 
operation is expressed as:
 u
xi fr and CR
xo therwise
id
son
id
son
id
son
,
,
,
()
, 
 




 (10)
where CR is the crossover rate with the range of (0, 1), 
d
rand
 is a random number from (1, 2,…, D).
The selection operator is enabled to choose better 
particle between u
i
son
 and x
i
 using the fitness function, 
which is expressed as: 
 u
xi ff uf x
i
xo therwise
id
son
id
son
i
son
id
son
,
,
,
(( )( ))
, 
 




 (11)
The equations of the VCPSO algorithm are 
expressed as follows:
 
vv cr Px cr Px
cr Px
id
n
id
n
id
n
id
n
gd
n
id
n
cd
n
id

  

1
11 22
33
 () ()
(
n n
), (12)
 xx v
id
n
id
n
id
n 

11
, (13)
where c
3
 is the accelerated coefficient, while the 
r
1
, r
2
 and r
3
 are random numbers in range (0, 1), 
respectively.
3  RESULTS AND DISCUSSIONS
3.1  Development of the Optimal ANN Models
The experimental data of the internal burnishing 
operation are presented in Table 2, in which the 
obtained data from experiments No. 1 to 26 are applied 
to construct the ANN models. The obtained data from 
experiments No. 27 to 38 are employed to test the 
accuracy of the developed models. The representative 
values of the experiments are shown in Fig. 4.
The working parameters of the ANN model are 
the NT, NN, TF, PM, TS, NL, and LF and their levels 
are shown in Table 3. 
As a result, the training function and the number 
of hidden neurons have the highest level (14) and 
the learning function has the minimum level (2). 
Consequently, the orthogonal array matrix with 256 
trials is employed to perform computational trials. The 
obtained results with the PE values less than 10 % for 
the ANN model are presented in Table 4. As a result, 
the optimal outcomes of the NT, NN, TF, PM, TS, NL, 
and LF are FFNN, TrainLM, 25, MSE, Logsig, 3, and 
Learn GDM, respectively (the trial No. 146). The PE 
values of the CYL, CIC, and SR are 3.46 %, 4.21 %, 
and 3.74 %, respectively. The network with a (4-25-
25-25-3) topology has been used for predicting the 
input-output parameters. The schematic view of the 
developed ANN model is shown in Fig. 5.
a) 
b) 
c) 
Fig. 4.  Representative results;  
a) the roughness profile at the No. 21, b) the SEM image of the 
burnished surface, c) the circularity at the No. 21
The R
2
 value and RMSE of the CYL model are 
0.9652 and 0.0532, respectively. The R
2
 value and 
RMSE of the CIC model are 0.9608 and 0.0621, 
respectively. The R
2
 value and RMSE of the SR model 
are 0.9648 and 0.0469, respectively. Therefore, it can 
be stated that the developed ANN models are adequate 
and significant.
To investigate the accuracy of the developed ANN 
model, a set of experiments is performed at random 
points. The comparisons between the predicted and 
experimental results are presented in Table 5. The 
errors of the CYL, CIC, and SR lie within the range 

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
159 Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel 
Table 2.  Experimental data for the internal burnishing process 
Experimental data 
for developing  
ANN models
No. D [mm] A [deg] Q [ml/h] P [MPa] CYL [µm] CIC [µm] SR [µm]
1 1.0 65 90 0.2 35.24 14.35 0.42
2 1.5 65 90 0.4 22.37 10.97 0.34
3 0.5 25 90 0.4 33.99 17.18 0.52
4 1.0 25 90 0.2 36.88 15.98 0.48
5 1.0 65 90 0.6 25.15 9.22 0.27
6 1.0 25 140 0.4 21.92 11.96 0.29
7 1.5 45 40 0.4 23.33 12.92 0.41
8 1.5 45 140 0.4 12.28 6.92 0.21
9 1.0 25 40 0.4 35.43 18.58 0.51
10 1.0 25 90 0.6 28.67 12.78 0.32
11 1.0 45 90 0.4 21.42 9.49 0.38
12 1.0 45 40 0.2 32.88 14.92 0.53
13 0.5 65 90 0.4 31.69 14.52 0.44
14 0.5 45 90 0.6 21.36 10.22 0.35
15 1.0 65 140 0.4 20.63 9.96 0.23
16 1.0 45 140 0.2 21.28 9.26 0.28
17 0.5 45 90 0.2 33.71 13.35 0.51
18 1.5 25 90 0.4 24.82 12.96 0.38
19 0.5 45 40 0.4 32.65 16.97 0.54
20 1.0 65 40 0.4 32.38 16.18 0.45
21 1.0 45 140 0.6 12.29 5.98 0.14
22 1.5 45 90 0.6 15.27 5.69 0.16
23 1.0 45 40 0.6 23.86 12.92 0.36
24 1.0 45 90 0.4 21.49 9.58 0.37
25 1.5 45 90 0.2 23.15 10.78 0.39
26 0.5 45 140 0.4 19.86 9.26 0.38
Experimental data 
for testing the 
accuracy  
of developed  
ANN models
27 0.5 40 50 0.5 29.36 15.83 0.53
28 0.5 40 70 0.4 29.29 14.27 0.51
29 0.5 30 90 0.3 34.12 15.51 0.54
30 0.5 45 90 0.6 22.31 10.66 0.36
31 1.0 45 70 0.4 23.82 11.18 0.41
32 1.0 40 90 0.5 20.45 9.22 0.33
33 1.0 45 100 0.6 17.66 6.99 0.23
34 1.0 50 100 0.4 20.43 8.93 0.34
35 1.5 40 90 0.5 16.84 7.85 0.27
36 1.5 50 110 0.3 17.59 8.78 0.31
37 1.5 30 90 0.6 21.11 9.24 0.21
38 1.5 60 70 0.2 28.52 13.46 0.44
of -2.05 % to 2.38 %, -2.17 % to 2.91 %, and -4.76 % 
to 3.70 %, respectively. The accepted deviations (less 
than 5.0 %) indicated that the proposed models could 
be applied to forecast the burnishing performances 
with good accuracy.
3.2 ANOVA Results for Technological Responses
Table 6 presents ANOVA results for the CYL model 
with significant factors. The computed participations 
of the D, A, Q, and P are 15.23 %, 4.12 %, 21.23 %, and 
16.52 %, respectively. The computed participations of 
the DQ, DP, AQ, and AP are 1.49 %, 3.85 %, 1.57 %, 
and 1.66 %, respectively. The computed participations 
of the D
2
, A
2
, and Q
2
 are 24.66 %, 1.76 %, and 7.38 %, 
respectively.  
Table 7 presents ANOVA results for the CIC 
model with significant factors. The computed 
participations of the D, A, Q, and P are 11.37 %, 7.64 
%, 20.97 %, and 11.69 %, respectively. The computed 
participations of the DA, DQ, DP , AP, and QP are 1.11 
%, 2.76 %, 3.15 %, 3.11 %, and 2.06 %, respectively. 

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
160 Le, V.-A. – Nguyen, T.-T.
The computed participations of the D
2
, A
2
, and Q
2
 are 
4.38 %, 22.78 %, and 7.69 %, respectively.
Table 8 presents ANOVA results for the SR 
model with significant factors. The computed 
participations of the D, A, Q, and P are 16.35 %, 6.73 
%, 24.81 %, and 19.81 %, respectively. The computed 
participations of the D
2
, A
2
, Q
2
, and P
2 
are 5.38 %, 
5.96 %, 3.84 %, and 7.31 %, respectively.
Table 3.  Operating parameters of the ANN models
Level
Network type
(NT)
Number of hidden 
neurons (NN)
Training function 
(TF)
Performance 
function (PM)
Transfer function 
(TS) 
Number of hidden 
layers (NL)
Learning function 
(LF)
1 CFNN 15 TrainBFG MSE Logsig 1 LearnGDM
2 ELNN 16 TrainBR MSEREG Purelin 2 LearnGD
3 FFNN 17 TrainCGB SSE Tansig 3 -
4 LRNN 18 TrainCGF - - -
5 19 TrainCGP - - -
6 20 TrainGD - - -
7 21 TrainGDM - - -
8 22 TrainGDA - - -
9 23 TrainGDX - - -
10 24 TrainLM - - -
11 25 TrainOSS - - -
12 26 TrainR - - -
13 27 TrainRP - - -
14 28 TrainSCG - - -
Table 4.  Training results for the developed ANN models
No. NT NN TF PM TS NL LF CYL [%] CIC [%] SR [%]
1 CFNN TrainBFG 16 MSE Logsig 1 LearnGDM 9.81 8.43 9.46
14 ELNN TrainOSS 20 MSEREG Tansig 2 LearnGDM 8.64 7.93 8.78
84 CFNN TrainLM 26 MSEREG Tansig 2 LearnGDM 5.71 6.35 5.92
98 CFNN TrainGDM 28 MSE Purelin 3 LearnGD 5.43 7.32 5.66
124 LRNN TrainSCG 18 SSE Tansig 2 LearnGDM 6.81 7.32 5.81
149 FFNN TrainLM 25 MSE Logsig 3 LearnGDM 3.46 4.21 3.74
168 LRNN TrainGDX 20 MSEREG Tansig 2 LearnGD 6.38 9.56 6.58
182 ELNN TrainSCG 22 MSE Purelin 3 LearnGD 4.44 6.72 4.94
226 ELNN TrainCGF 28 MSE Tansig 1 LearnGD 6.74 6.89 6.42
Table 5.  Comparative errors for the burnishing responses
No.
CYL [µm] CIC [µm] SR [µm]
Exp. ANN Err. [%] Exp. ANN Err. [%] Exp. ANN Err. [%]
27 29.36 29.69 -1.12 15.83 15.45 2.40 0.53 0.54 -1.89
28 29.29 28.98 1.06 14.27 14.58 -2.17 0.51 0.52 -1.96
29 34.12 34.53 -1.20 15.51 15.16 2.26 0.54 0.53 1.85
30 22.31 22.68 -1.66 10.66 10.35 2.91 0.36 0.37 -2.78
31 23.82 23.46 1.51 11.18 11.35 -1.52 0.41 0.42 -2.44
32 20.45 20.87 -2.05 9.22 9.39 -1.84 0.33 0.32 3.03
33 17.66 17.24 2.38 6.99 6.88 1.57 0.23 0.22 4.35
34 20.43 20.18 1.22 8.93 8.87 0.67 0.34 0.35 -2.94
35 16.84 16.59 1.48 7.85 7.97 -1.53 0.27 0.26 3.70
36 17.59 17.93 -1.93 8.78 8.61 1.94 0.31 0.32 -3.23
37 21.11 21.54 -2.04 9.24 9.17 0.76 0.21 0.22 -4.76
38 28.52 28.28 0.84 13.46 13.63 -1.26 0.44 0.43 2.27
Exp.: Experimental value; Err.: Error

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
161 Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel 
Fig. 5.  The architecture for the ANN models
Table 6.  ANOVA results for CYL model 
So. SS MS F value P value
Mo. 1240.283 88.592 21.302 < 0.0001
D 188.895 188.895 45.420 < 0.0001
A 51.100 51.100 12.287 0.0013
Q 262.072 262.072 63.015 < 0.0001
P 204.895 204.895 49.267 < 0.0001
DQ 18.480 18.480 4.444 0.0069
DP 47.751 47.751 11.482 0.0006
AQ 19.472 19.472 4.682 0.0038
AP 20.589 20.589 4.951 0.0024
A2 305.854 305.854 73.542 < 0.0001
Q2 21.829 21.829 5.249 0.0021
P2 91.533 91.533 22.009 0.0003
Res. 49.907 4.159 
Cor. 1288.606  
R
2
 = 0.9652
Mo.: Model; So.: Source; Res.: Residual; Cor.: Core total
Table 7.  ANOVA results for the CIC model
So. SS MS F value P value
Mo. 302.390 21.599 20.952 < 0.0001
D 34.382 34.382 33.351 < 0.0001
A 23.103 23.103 22.410 < 0.0001
Q 63.411 63.411 61.511 < 0.0001
P 35.349 35.349 34.290 < 0.0001
DA 3.357 3.357 3.256 0.0084
DQ 8.346 8.346 8.096 0.0053
DP 9.525 9.525 9.240 0.0045
AP 9.404 9.404 9.122 0.0046
QP 6.229 6.229 6.043 0.0057
D
2
13.245 13.245 12.848 0.0034
A
2
68.884 68.884 66.820 < 0.0001
Q
2
23.254 23.254 22.557 < 0.0001
Res. 12.337 1.031 
Cor. 314.727  
R
2
 = 0.9608
Table 8.  ANOVA results for the SR model
So. SS MS F value P value
Mo. 0.313 0.022 22.573 < 0.0001
D 0.051 0.051 51.671 < 0.0001
A 0.021 0.021 21.269 0.0003
Q 0.078 0.078 78.407 < 0.0001
P 0.062 0.062 62.605 < 0.0001
D
2
0.017 0.017 17.002 0.0214
A
2
0.019 0.019 18.835 0.0127
Q
2
0.012 0.012 12.135 0.0833
P
2
0.023 0.023 23.102 0.0037
Res. 0.012 0.001 
Cor. 0.324  
R
2
 = 0.9648
3.3  Parametric Influences
Fig. 6 indicates that the burnishing responses 
decrease with increasing nozzle diameter. As 
the nozzle diameter increases, more droplets can 
penetrate the burnishing region. The friction at the 
interfaces decreases, and the cooling-lubrication 
efficiency increases; hence, low CYL and CIC values 
are obtained. Moreover, the burnished surface is 
efficiently wetted and protected with a higher amount 
of oil mist; hence, the SR decreases. 
Fig. 7 presents the impact of the spray elevation 
angle on the CYL, CIC, and SR, respectively. For the 
CYL and CIC values, it can be stated that an increased 
spray elevation angle has a positive effect, but it 
turns negative at a certain amount (48 deg), while the 
turning point of 55 deg is applied for the SR. For low 
spray elevation angle, the penetration of the oil mist 
is ineffective, resulting in a lack of proper cooling-
lubrication efficiency at the interfaces. An uneven 
deformation is produced along the axial and radial 
directions; hence, higher values of the CYL and CIC 
are obtained. In addition, high friction at the interfaces 
causes a higher surface roughness. At a higher spray 
elevation angle, the mist flow is accurately delivered 
into the machining region, which results in decreased 
friction; hence, the CYL and CIC decreases. Higher 
cooling-lubrication efficiency causes a reduction in 
surface roughness.
Fig. 8 presents the impact of the Q on the CYL, 
CIC, and SR, respectively. A higher lubricant quantity 
increases the number of oil droplets entering the 
contact zone between the roller-workpiece. The 
cooling-lubrication efficiency will be improved due to 
friction reduction, which causes uniform deformation; 
hence, the CYL and CIC significantly decrease. The 
burnished surface is wetted and protected due to the 

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
162 Le, V.-A. – Nguyen, T.-T.
droplet size of the oil mist, while the number of 
droplets and spraying velocity increase. The small 
diameter increases the penetration ability into the 
burnishing region; hence, the cooling-lubrication 
efficiency is enhanced. The friction between the 
penetration of a higher amount of the oil mist; hence, 
the SR decreases.
Figs. 9 indicates that the reductions in the 
burnishing responses are found with increased air 
pressure. Higher pressure causes a reduction in the 
a)      b)      c) 
Fig. 6.  The impacts of the nozzle diameter on the burnishing responses; a) CYL and D, b) CIC and D, c) SR and D
a)      b)      c) 
Fig. 7.  The impacts of the spray elevation angle on the burnishing responses; a) CIC and A, b) CIC and A, c) SR and A
a)   b)   c) 
Fig. 8.  The impacts of the spray elevation angle on the burnishing responses; a) CYL and Q, b) CIC and Q, c) SR and Q
a)     b)     c) 
Fig. 9.  The impacts of the air pressure on the burnishing responses; a) CYL and P, b) CIC and P, c) SR and P

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
163 Investigation and Optimization of MQL System Parameters in the Roller-Burnishing Process of Hardened Steel 
burnishing tool and workpiece decreases; hence, the 
burnishing responses reduce.
3.4  Optimization Results
The proportions of the first principal component is 
92.7 %, followed by the second component (4.7 %) 
and the third component (2.6 %), respectively. The 
weight values are calculated using the squares of 
subsequent eigenvectors of the three components. The 
weight values of the CYL, CIC, and SR are 0.48, 0.34, 
and 0.18, respectively. 
For the high-pressure bushing components, the 
predefined surface roughness of 0.1 µm is selected. 
The 3D plot produced with the aid of the VCPSO is 
shown in Fig. 10. As a result, the optimal values of the 
D, A, Q, and P are 1.5 mm, 50 deg, 140 ml/h, and 0.6 
MPa, respectively, while the corresponding outcomes 
of the CYL, CIC, and SR are 10.07 µm, 4.04 µm, and 
0.10 µm, respectively (Table 9). The improvements in 
the CYL, CIC, and SR are 53.14 %, 57.83 %, and 9.64 
%, respectively, as compared to the initial values. The 
CYL, CIC, and SR are decreased by 85.74 %, 78.14 
%, and 97.40 %, respectively, as compared to the pre-
machined state. 
Fig. 10.  3D plots produced by the VCPSO
4 CONCLUSIONS
In this study, a new MQL-based burnishing process 
has been proposed and optimized. Instead of 
traditional process parameters, the inputs considered 
were MQL operating parameters, including the nozzle 
diameter, the spray elevation angle, the lubricant 
quantity, and the pressure value of the compressed 
air. The quality indicators of the burnished hole, 
including the cylindricity and circularity, were then 
addressed and enhanced. The ANN approach was 
employed to develop the performance models instead 
of conventional regression models. A new evolutional 
algorithm entitled VCPSO algorithm was applied to 
find the optimal solution. 
The academic contributions can be expressed as 
follows:
The proposed optimization technique comprising 
ANN, GRA, and VCPSO used in this study 
effectively solves the complex optimizing problems 
and determines optimal outcomes. The developed 
approach possesses various advantages, including 
low experimental costs, decreased human efforts, and 
easy application by means of Matlab software. The 
optimizing method can be extensively employed to 
solve the optimization issues for not only burnishing 
operations but also other machining processes.
The impacts of the MQL operating parameters 
(the nozzle diameter, the spray elevation angle, 
the lubricant quantity, and the pressure value of 
the compressed air) on the geometric deviations 
(cylindricity and circularity) and the surface roughness 
have been thoroughly analysed. The obtained 
knowledge can help machine operators deeply 
understand the physical insights in the developed 
burnishing operation.
The optimized findings can be applied in the 
burnishing operation to enhance the machining 
performances. Moreover, the obtained results revealed 
that the MQL system could be employed to enhance 
the machining quality of internal holes in various 
components.
The optimization issues addressing the 
cylindricity and circularity with a predefined 
constraint of the surface roughness are realistic and 
Table 9.  Optimization results produced by the VCPSO
Method D [mm] A [deg] Q [ml/h] P [MPa] CYL [µm] CIC [µm] SR [µm]
Initial values 1.0 45 90 0.4 21.49 9.58 0.37
Optimal values 1.5 50 140 0.6 10.07 4.04 0.10
Improvements [%] 53.14 57.83 72.97

Strojniški vestnik - Journal of Mechanical Engineering 68(2022)3, 155-165
164 Le, V.-A. – Nguyen, T.-T.
reliable compared to the simultaneous optimizing 
three objectives.
The analysed outcomes of the current study can 
be effectively and efficiently utilized as significant 
references for future investigations and developing 
expert systems regarding MQL-assisted internal 
burnishing processes.
The industrial contributions can be expressed as 
follows.
In this work, an efficient burnishing operation 
under the MQL condition has been developed to 
enhance the machining quality of the internal surface. 
The successful implementation in the hardened steel 
is utilized to prove the effectiveness and potential 
application of the proposed burnishing process. The 
developed finishing operation can be effectively and 
efficiently applied to fabricate internal holes.
The constructed ANN models of the CYL, CIC, 
and SR were significant and accurate. The proposed 
correlations could be effectively applied to forecast 
the burnishing responses in industrial applications. 
The experimental costs and efforts can be saved with 
the aid of the proposed ANN models.
The 3D plots showing global relationships 
among machining performances can support machine 
operators in selecting the optimal values of the MQL 
operating factors, circularity, and cylindricity under 
various constraints of the surface roughness for 
different purposes.
The influences of the MQL system parameters 
on energy consumption and production costs have 
not been presented. A comprehensive optimization 
considering more objectives will be explored in future 
works.
5  ACKNOWLEDGEMENTS
This research is funded by Vietnam National 
Foundation for Science and Technology Development 
(NAFOSTED) under grant number 107.04-2020.02.
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